Abstract
Group recommendations are a specific case of recommender systems (RS), where instead of recommending for each individual independently, shared recommendations are produced for groups of users. Usually, group recommendation techniques (i.e., group aggregators) are built on top of common "single-user"RS and the resulting group recommendation should reflect both the overall utility of the recommendation as well as fairness among the utilities of individual group members.
Off-line evaluations of group recommendations were so far resolved either as a tightly coupled pair with the underlying RS or in a decoupled fashion. In the latter case, the relevance scores estimated by underlying RS serves as a ground truth for the evaluation of group aggregators.
Both coupled and decoupled evaluation may suffer from different biases that provide illicit advantages to some classes of group recommending strategies. In this paper, we focus on the decoupled evaluation protocol and possible polarity bias of the underlying RS.
We define polarity bias as situations when RS either locally or globally under-estimate or over-estimate the true user preferences. We propose several polarity de-biasing strategies and in the experimental part, we focus on the capability of group aggregation strategies to cope with the polarity biased input data.